Abstract

Single-cell functional proteomics assays can connect genomic information to biological
function through quantitative and multiplex protein measurements. Tools for single-cell
proteomics have developed rapidly over the past 5 years and are providing approaches
for directly elucidating phosphoprotein signaling networks in cancer cells or for
capturing high-resolution snapshots of immune system function in patients with various
disease conditions. We discuss advances in single-cell proteomics platforms, with
an emphasis on microchip methods. These methods can provide a direct correlation of
morphological, functional and molecular signatures at the single-cell level. We also
provide examples of how those platforms are being applied to both fundamental biology
and clinical studies, focusing on immune-system monitoring and phosphoprotein signaling
networks in cancer.

Cell-to-cell variation and single-cell functional proteomics analysis

Non-genetic cellular heterogeneity is a universal feature of any cell population [1,2]. Although this heterogeneity is often ascribed to some process (such as stochastic
gene expression), it is also intrinsic to the finite nature of a single cell [3]. This heterogeneity is not without consequences; for example, it can contribute to
the diversity of an immune response or to the emergence of therapeutic resistance
in cancers. However, the detailed role of cellular heterogeneity in such processes
is not always easy to capture. If some parameter is measured on a statistical number
of 'identical' single cells, that parameter can almost always be used to stratify
those cells into multiple populations. Whether the variance in the assayed parameter
is biologically relevant may be debatable. Parameters for which the variance is thought
to have high biological relevance are the levels of functional proteins. These include
the signaling proteins (such as cytokines) that are secreted by immune cells, or the
phosphorylated kinases and related effector proteins that comprise the heart of growth
factor signaling networks within cells.

A single-cell functional proteomics assay is one that measures the quantity and functional
state (such as phosphorylation) of a given protein or panel of proteins across many
otherwise identical cells. A measurement of the average level of a protein requires
many single-cell measurements. Such measurements, if compiled as a histogram of the
frequency of observation versus the measured levels, reflect the fluctuations of that
protein. Functional protein fluctuations can reflect changes in cellular activity,
such as immune-cell activation or the activation or inhibition of protein signaling
networks within, for example, tumor cells. However, the usefulness of fluctuations
significantly expands with absolute quantification and increased numbers of proteins
assayed per cell (multiplexing).

When multiple proteins are assayed from single cells, protein-protein correlations
and anti-correlations are directly recorded. For cell-surface markers, such measurements
provide a way to enumerate and sort highly defined cellular phenotypes. A multiplex
analysis of secreted effector proteins from immune-cell phenotypes can provide a powerful
view of immune-system function. For intracellular signaling networks, such as those
associated with growth factor signaling, correlations and anti-correlations between
phosphoproteins can indicate activating and inhibitory interactions, respectively.
With increased multiplexing, such measurements increasingly resolve the structure
of signaling networks. If the measurements are truly quantitative, it becomes possible
to assess how perturbations to cells influence changes in the chemical potential of
the measured proteins. This, in turn, allows the introduction of predictive models
derived from physicochemical principles.

Single-cell functional proteomics can connect genomic information with biological
context and biological function. For example, certain classes of genetically engineered
immune cells are increasingly used for certain anti-cancer therapies. This clonal
population of cells can show great functional heterogeneity [4,5]. That heterogeneity, which can be characterized by single-cell proteomics, arises
from many epigenetic factors (biological context), such as exposure to specific cell
types or to signaling proteins. This and other examples are discussed in detail below.

Here, we describe emerging technologies and their associated applications that are
designed to characterize cellular heterogeneity by single-cell functional proteomics.
We first provide an overview of the rapid development of single-cell proteomics tools
that has occurred over the past half decade. We then discuss specific biological or
clinical challenges that are either uniquely or most easily addressed by single-cell
functional proteomics. These challenges include basic biology studies, such as the
kinetics of T-cell activation, or the identification of effector proteins associated
with cellular motility. Clinical applications include advanced immune monitoring of
patients with a variety of disease conditions, ranging from HIV to cancer. Cancer
biology applications include experiments aimed at resolving how targeted therapeutics
alter the phosphoprotein signaling networks that are hyperactivated in many tumors.
Each problem provides a venue for discussing platform advantages and limitations.
We focus on multiplex microfluidics/nanotechnology-based platforms as these tools
are proving uniquely suited for quantitative, single-cell functional proteomics.

Single-cell functional proteomics technologies

Single-cell functional proteomics tools range from flow cytometry to microfluidics-based
platforms, many of which are listed and briefly characterized in Table 1. An ideal tool reports on the level of a given protein in copy numbers per cell,
with a small uncertainty, a high level of sensitivity, and the capacity to analyze
large numbers of cells quickly. The value of absolute quantification is that it enables
direct comparisons across platforms, cell types, time points, clinical samples, and
so on. However, many platforms enable quantification only in relative units, or allow
for the identification of only the fraction of the cells that express a given protein.
Other characteristics, such as the level of multiplexing, the types of proteins that
can be assayed (such as cytoplasmic, membrane, or secreted), or the ability to integrate
functional assays (such as cell motility) with proteomics assays, are also important
attributes.

Single-cell functional proteomics tools may be classified into three classes (Table
1). The first class comprises cytometry methods (Figure 1b illustrates flow cytometry), which have evolved over 40 years. The basic idea is
to label specific cellular proteins. The cells are then suspended in bulk, and then
analyzed, one by one, for the presence of the label. For fluorescence flow cytometry
(FFC) (or fluorescence activated cell sorting (FACS)), cellular proteins are labeled
with fluorescent antibodies [6,7]. The degree of multiplexing is limited to around 15 by the availability of spectrally
distinct fluorophores. The recently developed mass cytometry [8] expands multiplexing to more than 30 by using transition metal mass labels, instead
of fluorophores, followed by mass spectrometric analysis of individual cells. For
these tools, most assayed proteins are cell surface markers, rather than functional
proteins. Intracellular staining (ICS) [9], which requires blocking protein secretion and fixing the cells, can be coupled with
cytometry to interrogate for the relative levels of functional proteins such as cytokines
or phospho-kinases. Cytometry methods (particularly FFC and FACS) readily handle large
numbers of cells, and so can be used to identify (and sort) relatively rare cell types.
Cytometry tools capable of a high degree of multiplexing are very powerful, but are
also expensive, and usually require a staffed facility for their operation.

Figure 1.Selected tools for single-cell functional proteomics. Three technology platforms are illustrated, along with data that highlight the unique
strengths of each platform. (a) All platforms start with a single-cell suspension. (b)(i) Intracellular staining (ICS) flow cytometry for assaying for secreted (functional)
proteins requires blocking cell secretion during an incubation step, fixing the cells,
and then permeabilizing the cells to enable antibody staining. (b)(ii) Proteins are
colorimetrically detected by streaming the cells, one at a time, through multicolor
laser excitation. (b)(iii) A flow cytometry scatter plot showing the correlation of
two effector proteins detected from stimulated CD8+ T cells. This plot reflects the excellent statistics achievable using this technique
(adapted from [5] with permission). (c)(i) Microengraving assays start by isolating single cells into microwells, several
thousand of which are patterned onto a single chip. A glass substrate that is microengraved
with various capture antibodies covers the microwells. The substrate can be replaced
at various times to reveal protein-specific secretion kinetics. The phenotype of the
cells can also be determined by imaging, using fluorophore-labeled antibodies against
specific cell-surface markers. (c)(ii) Secreted protein levels are measured by developing
the microengraved slides with fluorophore-labeled, secondary antibodies and correlating
the fluorescence signal with the microchamber address. (c)(iii) Assembled traces reveal
the secretion kinetics for three proteins from a specific T-cell phenotype. The color
coding key is provided in the colored circle at top left. Adapted from [37] with permission. (d)(i) Single-cell barcode chip (SCBC) assays also begin by isolating cells within small-volume
microchambers. Flexibility of microfluidics design enables individual cells to be
lyzed for analysis of cytoplasmic proteins and membrane and secreted proteins. Proteins
are captured on miniature antibody barcode arrays. A full barcode representing the
panel of proteins to be assayed is incorporated into each microchamber. (d)(ii) SCBC
assays yield data on single cells and on small cell populations. Three developed barcodes
are shown; the yellow number indicates the numbers of cells in the associated microchamber.
(d)(ii) Statistical analysis of single-cell data collected from model brain cancer
cells. Top: scatter plot showing the correlation of two phosphoproteins. The black
or red dots represent data from microchambers containing 0 or 1 cells, respectively.
Bottom: scatter plots show the statistical uniqueness of the 0-cell, 1-cell, and 2-cell
datasets for p-EGFR. a.u., arbitrary units. Adapted from [28] with permission.

Surface methods (Table 1) for single cell functional proteomics include the established and relatively simple
and inexpensive ELISpot technique for detecting protein secretion from live cells
[9]. Cells are immobilized on an antibody-coated surface, and sandwich-type immunoassays
are utilized to detect secreted proteins in the vicinity of individual cells.

Microfluidics technologies constitute the third class of tools. Common advantages
of microfluidics tools are that they can often be cheaply manufactured in large quantities,
they can handle very small numbers of cells and require only tiny quantities of expensive
reagents, and they may often be customized to allow for on chip incubation, cell lysis,
and so on. For single cell proteomics, microfluidics platforms fall into two groups
- those in which the cells are stained to identify specific proteins, and those for
which proteins are released from the cells and measured using surface immunoassays.
The first group includes the image cytometry, cell-array, and micro-droplet techniques.
Early variations of such tools detected proteins from single cells by imaging stained
cells, or by flowing the labeled cells or cell-encapsulation droplets through a microfluidic
channel designed to allow fluorescence detection. These were basically microchip versions
of FFC or FACS [10]. More recent approaches have significantly diverged to take advantage of some of
the unique aspects of microfluidics. For example, cells can be spatially segregated
into large arrays (cell arrays [11-15]), or they can be entrained within arrays of drops [16-18]. Such manipulations are followed by immunostaining of membrane proteins, followed
by automated imaging to quantify single-cell fluorescence signals. These approaches
can offer control over the cell environment before analysis, which make them attractive
screening tools. One disadvantage of these and other cell-staining approaches [19,20] is that they have limited multiplexing capacity.

The most advanced microfluidic single-cell proteomics tools use surface-immobilized
antibodies for separating protein detection from cell manipulation (Figure 1c,d). This approach has several advantages: it can yield increased multiplexing capacity,
it can be extended to assays for secreted, cytoplasmic, and membrane proteins, and
measurements of cellular functions can be integrated with the proteomics assays. The
experimental challenge is that a given cell may only produce between a few hundred
and a few thousand copies of a protein of interest - such numbers are typical for
many phosphoproteins or secreted signaling proteins. The solution is to enclose the
cell within a microenvironment with a volume of about 1 nl. In this way, the resultant
protein concentration can be sufficiently high to allow detection with standard immunoassays.
The beauty of microfabrication is that such tiny volume assays can be repeated many
times, in parallel, on a single microchip. The microengraving approach developed by
Love's group (Figure 1c) [21-23] uses small volume microwells in an array format to isolate and culture single cells.
A 'microengraved' (antibody-coated) substrate is used to cap the microwell array and
to capture secreted proteins. Proteins are detected using sandwich-type ELISA immunoassays.
Different fluorophores colorimetrically distinguish between different detection antibodies
to allow the simultaneous detection of about three secreted proteins. The microengraved
substrate can be replaced multiple times in situ, thus enabling kinetic studies (Figure 1c(ii,iii)) at the single-cell level. The multiplexing capacity of the microengraving
method can be increased using fluorophore-labeled antibody staining of membrane proteins;
fluorescence imaging of the captured cells yields information on membrane protein
levels (to identify cellular phenotypes), and the microengraved substrate assays for
secreted proteins (to assess cellular function).

A related approach is the single-cell barcode chips (SCBCs). The basic concept is
to pattern a many-element capture antibody array in each single-cell microwell so
that different proteins are detected at different designated array spots. The key
enabling technology of SCBCs is the miniature antibody arrays. A related challenge
is that antibody arrays are not stable to the physical conditions of microfluidics
device fabrication. The solution has been to couple the technique of DNA-encoded antibody
libraries (DEAL) [24] with microfluidics-based flow patterning. Specifically, an elastomer film is molded
so that it contains a series of long, serpentine channels. It is adhered to the top
of a glass slide. Solutions containing a different single-stranded DNA (ssDNA) oligomer
are flowed through each channel. Those solutions evaporate. The molded elastomer is
then removed, leaving a series of 10 to 20 µm wide stripes of different ssDNA oligomers
across the glass substrate. A second elastomer layer, patterned with between 300 and
10,000 microchambers for single-cell assays, is adhered to the glass slide. The design
is such that each microchamber contains a full complement of ssDNA stripes. Just before
use, these miniature ssDNA arrays are converted into antibody arrays using a cocktail
of complementary ssDNA-labeled antibodies. The resultant antibody array (the barcode)
[25] provides the detection technology for SCBCs (Figure 1d(ii)) [4,26]. Up to 20 functional proteins have been assayed per cell [5], and the limit is probably around 100. Specific SCBC designs enable cell lysis, thus
allowing cytoplasmic, membrane, and secreted proteins to be assayed from the same
single cell. SCBC assays can yield absolute protein level quantification [27] and access to discrete cell populations (one cell, two cells, three cells and so
on) [28] (Figure 1d(iii)). Both the SCBC and microengraving platforms can be integrated with multicolor
FACS to enable the integration of phenotype analysis with functional proteomics [28]. Quantitative data comparison across different SCBC assays [29] allows clinical studies or investigations in which statistical cell behaviors are
compared across a perturbation series.

Most microfluidics tools enable the single cells to be imaged. When integrated with
proteomics measurements, this can enable several interesting assays, such as correlating
cell motility or cell-cell interactions [28,30] with specific protein levels. Unlike flow cytometry analyses, cells can remain in
their native morphology so that cell size, spreading, or motility can be correlated
with proteomic signature for each cell assayed [30]. The ability of a cytotoxic T cell to kill the target cell can be directly visualized
under an optical microscope. Once conducted in a microengraving device, this allows
direct comparison of cytolic activity with the protein profile of the same T cell
[31]. Finally, cells can be recovered from these types of assays for additional analysis,
or for establishing clonal cell lines with desired properties [23]. This direct coupling of many different assays on the same single cells is, so far,
a unique attribute of these microfluidics platforms.

Applications to immune monitoring and function

Immune cells are classified along the hematopoietic lineage, starting with myeloid
and lymphoid lineages. A triumph of immune system biology has been the identification
of cell surface markers that allow, by FACS, the enumeration and sorting of specific
immune-cell phenotypes from blood or tissues. For example, a cytotoxic T cell is defined
by the cell surface markers Cluster of Differentiation (CD)3, CD45, and CD8, with
additional markers specifying the antigen specificity of the T cell receptor (TCR)
or providing further phenotypic classification, such as effector memory. However,
functional information requires assays of secreted effector proteins (such as cytokines
and cytotoxic granules) that mediate the tasks of target killing, self-renewal, recruitment
of other immune cell types, and inflammation. Because of the variety of potential
pathogen targets, cellular immunity is functionally heterogeneous. Recent studies
using different single-cell proteomics platforms have begun to capture and characterize
this heterogeneity.

The function of an immune cell is largely delineated by a range of proteins it produces.
Early efforts to profile multiple immune effector functions of single immune cell
function used ICS FFC. Betts et al. [32] measured five functions (degranulation and levels of interferon (IFN)-γ, macrophage
inflammatory protein (MIP)-1b, tumor necrosis factor (TNF)-α, and interleukin (IL)-2)
from single HIV-specific CD8+ T cells collected from chronically HIV-infected individuals and people whose HIV infection
has not progressed over a long term (called non-progressors or elite controllers).
The number of effector functions displayed in T cells from people with chronic HIV
was limited relative to those from non-progressors, and the number of functions ('polyfunctionality')
was inversely correlated with viral load. Another example of the use of ICS FFC was
by Darrah et al. [33], who showed that the degree of protection against Leishmania major infection in mice is predicted by the frequency of CD4+ T cells simultaneously producing IFN-γ, IL-2, and TNF-α. More recent studies have
used ICS mass cytometry. For example, Newell and coworkers [34] used this approach to assay 17 membrane protein markers, 6 intracellular cytokines
and 2 cytotoxic granules from stimulated CD8+ T cells from healthy patients. They found that the cytokine secretion profiles were
almost statistically distributed across the individual cells, but there were distinct
niches occupied by virus-specific cells.

Microfluidic functional proteomics has been used for longitudinal monitoring of patients
undergoing adoptive cell transfer (ACT) trials, a form of immunotherapy for metastatic
melanoma. Ma and coworkers [4] used SCBCs to compare the functional diversity of tumor antigen (MART-1)-specific
CD8+ T cells collected from the blood of a melanoma cancer patient with CD8+ T cells collected from healthy donors. At the time of collection, the patient was
participating in an ACT trial that used TCR-engineered T cells specific for the MART-1
melanosomal antigen [35]. In this therapy, the TCR-engineered T cells are expanded ex vivo and infused into the patient with the aim that the T cells will drive an anti-tumor
immune response. Ma's team assayed a panel of 12 secreted proteins and found a large
(albeit not statistically random) range of functional phenotypes within a tightly
defined T-cell phenotype [4]. A follow-up kinetic study [5] helped define some of this functional diversity (Figure 2). The authors [5] studied three melanoma cancer patients participating in the same ACT trial and combined
19-plex SCBC functional (secreted) protein assays with 10-color FACS to measure the
functional evolution of specific T-cell phenotypes at 5 to 10 time points over a 90-day
trial (Figure 2a). These measurements led to several conclusions. First, for a given patient and T-cell
phenotype, if all single-cell data from all time points were co-analyzed, a level
of functional coordination was resolved, meaning that the T cells could be loosely
classified according to biological behaviors, such as anti-tumor or pro-inflammatory.
Second, the most polyfunctional cells dominated the immune response (Figure 2b). Roughly 10% of the cells of a given phenotype secreted five or more different proteins.
For any one of those proteins, those highly functional cells secreted, on average,
100-fold more protein copies than the less polyfunctional cells. Thus, for a given
phenotype, 10% of the cells dominated the overall immune response by 10-fold. This
led to the defining of a polyfunctionality strength index (Figure 2c). Interestingly, although the cellular population dynamics or phenotype changes (such
as naïve or central memory) over the course of the trial did not yield clear clinical
correlates, the polyfunctionality kinetics did correlate with clinical observations,
providing feedback for potentially improving the ACT trial design. This collective
work over the past decade has refined the notion that the quality of a T-cell immune
response is best captured by the functional performance of the T cells, rather than
their quantity [36].

Figure 2.Integrated FACS/SCBC phenotypic/functional proteomic analysis of tumor-antigen-specific
T-cell populations collected from a melanoma cancer patient participating in an ACT
trial. (a) Measurement protocol. MART-1 tumor-antigen-specific CD8+ T cells are separated from the blood of the patient using 10-parameter FACS sorting
and then loaded onto an SCBC for assaying 19 secreted effector proteins. (b) Analysis of SCBC data. Unsupervised clustering of the single-cell proteomic data (tree,
left) reveals coordinated behaviors that reflect specific immune functions. Correlation
coefficients, calculated from single cell assays, are provided for proteins within
the specified groupings (In group) and outside those groupings (Out group). The scatter
plot (right) shows correlations between two anti-tumor effector proteins (IFN-γ and
TNF-α) and also shows that the roughly 10% of the cell population that secretes five
or more different proteins are also about 100-fold more active for any given protein,
and so dominate the immune response for that phenotype. (c) The population kinetics of the TCR-engineered MART-1+ CD8+ T cells, as a percentage
of CD3+ T cells (orange solid curve), along with the polyfunctional index (pie chart
areas) for tracking population of the MART-1+ CD8+ T cells secreting five or more
proteins. The pie chart composition reflects the relative abundances of those proteins.
GB refers to the protein Granzyme B. The dynamics of the polyfunctional cells showed
much stronger correlations with the observed clinical responses in the patients. Adapted
from [5] with permission.

Microfluidics platforms offer the unique capacity for coupling cell adhesion, spreading,
and migration assays with multiplex functional proteomics from the same single cells.
This is because cells can be incubated and observed within the same microenvironment
in which the protein assays are executed. Such assays have relevance for understanding
cancer cell behaviors. Cell migration, for example, can be influenced by certain of
the cytokines more commonly associated with immune cells. Lu et al. [30] used an SCBC-type antibody array coupled with custom-designed microchip (Figure 3), and identified a few cytokines (IL-6, IL-8, and monocyte chemotactic protein (MCP)-1)
that correlated with cell motility.

Figure 3.Multiplexed proteomics for co-measurement of cell migration and cytokine secretion
of the same A549 (model lung carcinoma) cancer cells. (a) Light field images showing migration of three single cancer cells within microfluidic
channels collected at 0 (before) and 24 (after) hours. (b) Heatmap: each column is a single-cell assay; each row is an assayed parameter. Cell
migration distance (top row) is shown with the entire protein secretion profile (lower
14 rows). Approximately 1,000 single cells were assayed. (c) Scatter plots showing how the levels of three proteins (MCP-1 and IL-6) varied with
cell migration distance. a.u., arbitrary units. Adapted from [30] with permission.

Love and colleagues used microengraving to carry out two sets of studies that coupled
functional behaviors with functional proteomics on single T cells [31,37]. In the first [31], they measured cytolytic activity of CD8+ T cells by performing live-cell imaging of these cells cultured together with single
target cells in a microengraving device. This allowed the killing ability of individual
T cells to be directly correlated with the production of multiple cytokines, and it
revealed a discordance between cytokine secretion and cytolysis. The authors [31] found that the majority of in vivo primed, circulating HIV-specific CD8+ T cells were discordant for cytolysis and secretion of cytokines, notably IFN-γ, when
encountering cognate antigen presented on defined numbers of cells. In their second
study [37], they investigated the kinetics of cytokine production using serial analyses of single
primary human T cells under various conditions (Figure 1c). They showed that for multifunctional T helper 1-skewed cytokine responses (IFN-γ,
IL-2, and TNF-α), cells predominantly release those cytokines sequentially, rather
than simultaneously. These kinetic trajectories were associated with states of cell
differentiation, suggesting that transient programmatic activities of many individual
T cells contribute to sustained, population-level responses.

The value of absolute quantification was demonstrated by Shin et al. [38], who used a 12-plex SCBC assay to investigate how the secretome of lipopolysaccharide-stimulated
macrophage cells responded to neutralizing antibody perturbations. They reported on
the use of statistical-physics-derived models as a means for correctly predicting
how specific secreted protein levels would vary with the perturbations. We cover related
concepts below in our discussions of phosphoprotein signaling networks.

Applications to intracellular signaling networks

For many cancers, genomic surveys are revealing a rich molecular landscape of altered
signal transduction cascades that often cluster along a set of druggable core pathways.
In fact, these pathways contain many of the targets of the newer generations of targeted
cancer therapies [39]. However, the translation of genomic data into effective clinical treatments has
not been straightforward. This is at least partly because non-genetic cell-to-cell
variability is profound in drug responses and resistance development, yet it cannot
be readily captured from genome sequencing data. A recent editorial [40] has pointed out that capturing the functional protein signaling networks may prove
valuable for this purpose, because it is those 'signaling proteins, not the genes
per se, that are responsible for the phenotypes of tumors and for the emergence of therapeutic
resistance'. Single-cell proteomics provides the most direct approach for elucidating
signaling network structure and coordination, and for interrogating how that coordination
is disrupted by drugs. It thus may provide a powerful tool for translating genomic
information into effective clinical practices for many highly challenging types of
cancer [41].

An early single-cell study of phosphoprotein signaling [42] used ICS FFC to assay, in various cancer cells, the cytokine responses of six phosphoproteins,
mostly from the signal transducers and activators of transcription (STAT) family.
Signaling network heterogeneity and network remodeling was observed in both normal
cells in a hematopoietic compartment [43] and cancerous cells such as acute myeloid leukemia [42], suggesting that cells could be classified according to functional phenotype. There
have been other highly multiplex studies of phosphoprotein signaling networks using
flow (or mass) cytometry [44] or image cytometry [19] over the past decade, and more recent work using SCBC platforms [27,28,45]. Such a sparse literature (especially compared with the routine use of cytometry
techniques for cellular phenotyping) highlights the difficulty of these assays, even
though the specific studies have illustrated their value. We now turn to discussion
of this value, within the specific context of cancer pathways.

Cancer pathway models are essentially maps of the protein-protein interactions that
describe the flow from a cell signaling trigger (ligand-receptor binding) to functional
behaviors, such as cell division or apoptosis. These pathways are often assembled
from diverse datasets (high-throughput data on cell populations, integrated with small
interfering RNA perturbations, knockout models, and so on) to yield maps in which
the nodes are functional proteins and the edges are inhibitory or activating interactions.
These models generally assume linear relationships between upstream effector proteins,
ATP, and nutrient levels and activation downstream. However, most signaling cascades
behave as excitable devices with thresholds, enabling them to integrate diverse temporal
and spatial inputs to produce specific signaling responses [46]. Single-cell proteomics discerns much of this detail, and, if truly quantitative,
can yield simplifying approaches towards understanding how such pathways function
(Figure 4).

Figure 4.Phosphoprotein signaling networks from multiplex, quantitative single-cell proteomics. All data represented are uniquely measured at the single-cell level. (a) A Monte-Carlo simulation of fluctuations that represent the copy numbers per cell
of an activated (such as phosphorylated) form of a protein, as that protein is involved
in increasing numbers of regulatory processes. On the right are the experimentally
measured fluctuations of HIF-1α from model GBM cancer cells as these cells are exposed
to different O2 partial pressures. The increasingly important role of HIF-1α under hypoxic conditions
is evident. Reproduced from [45]. (b) Scatter plot showing protein-protein correlations for two phosphoproteins. The black
and red dots represent measurements from 0-cell and 1-cell SCBC microchambers, respectively.
Reproduced from [28]. (c) A protein-protein correlation network for model GBM cancer cells following epidermal
growth factor (EGF) stimulation (top), and following EGF stimulation + erlotinib (anti-EGF
receptor) inhibition (bottom). The weight of the network edges reflects the correlation
strength, and a red edge indicates an anti-correlation. Reproduced from [27]. (d) Collective signaling modes, as determined by the eigenvectors of the single-cell protein-protein
covariance matrix. Shown are the eigenvectors associated with mTORC1 signaling in
model GBM cells, as pO2 is varied. The composition of the green, red, and blue eigenvectors (top plot) is
given in the pie charts below for each value of pO2 investigated. The amplitude of the mTORC1 associated eigenvectors shows a minimum
between 1.5% and 2% pO2, indicating the loss (and undruggability) of that signaling within this narrow window
of pO2 values. Note that HIF-1α is strongly associated with mTORC1 signaling above 2% pO2, but not below 2% pO2, indicating a switch in the structure of the signaling network. The cells studied
were model GBM cell lines containing the EDFR variant III (vIII) oncogene (U87 EGFRvIII;
panels a, b, d) or the EGRFvIII oncogene plus loss of the phosphatase and tensin homolog
(PTEN) tumor suppressor gene (EGFRvIII PTEN). Reproduced from [45] with permission.

Population heterogeneity can arise from factors such as the stochastic nature of intracellular
events controlled by low-copy-number transcription factors [47] or through cell-cell interactions [48,49]. The net result is often high-amplitude fluctuations at the single-cell level but
stable distributions across a population [50]. The concept of a stable population existing in the presence of random fluctuations
is reminiscent of many physical systems that are successfully understood using statistical
physics. Thus, tools derived from that field can probably be applied to using fluctuations
to determine the nature of signaling networks. This approach contrasts with traditional
biology thinking, which might seek to classify the population into functional phenotypes.

Wei and coworkers [45] reported simulations to account for how an increasing signaling activity of a hypothetical
protein would be reflected in the fluctuations of the activated state of that protein
(Figure 4a). They used a mean field theory, which treated the increasing signaling activity
of the hypothetical protein as arising from the statistically averaged (mean field)
influences of effector proteins. As the activity increases, the fluctuations shift
to higher average copy numbers and are increasingly dispersed. The simulations captured
how the experimentally measured fluctuations of hypoxia inducible factor (HIF)-1α
in single glioblastoma multiforme (GBM) cancer cells evolve as the cells were exposed
to increasingly hypoxic conditions. HIF-1α is, in fact, steadily activated as the
cells transition from normoxia to hypoxia [51]. This conclusion can be drawn by simply inspecting the HIF-1α fluctuations.

Quantitative, multiplexed assays can also provide protein-protein correlations. This
means that one can use statistical models that explicitly account for protein-protein
interactions (Figure 4b,c) and begin defining the state of the signaling network. Shin et al. [38] developed a quantitative Le Chatelier principle that relates how the changes in average
signaling protein levels following a weak perturbation to a cell correlate to the
changes in the chemical potentials of those proteins. The Le Chatelier principle states
that a stable system will respond to a weak perturbation so as to restore that stability.
The theory is summarized by the matrix equation . Here, is a column vector with P components representing the average protein levels of the P assayed proteins; β is 1/kBT, where kB is Boltzmann's constant and T is temperature; Σ is a P × P matrix where each element is the experimentally measured covariance of a specific
protein Pi with another specific protein Pj; and Δµ is a column vector whose P components describe the change in the chemical potentials of the P proteins, due to a change in external conditions (the perturbation). If the predicted
changes in protein levels match experiment, the implication is that the signaling
network is described by a stable state and responds to a weak perturbation so as to
restore that state. If the calculation does not match experiment, then either the
perturbation is strong or the signaling network is not stable. The theoretical tools
were coupled with single-cell proteomics assays of mammalian target of rapamycin (mTOR)
complex1 (C1) and HIF-1α signaling in model GBM cancer cells, to capture the response
of these networks to the transition from normoxia (21% O2 partial pressure (pO2)) to hypoxia (1% pO2) (Figure 4d). mTORC1 signaling was identified as one stable state above 2% pO2 and as a different stable state between 1.5% and 1% pO2, with a switch between those two states near 2 to 1.5% pO2. Within this narrow window of pO2, the models predicted that mTORC1 would be unresponsive to inhibitors, but that it
could be drugged at higher or lower pO2. These surprising predictions were found to be correct in both cell lines and tumor
models [45].

These results have several implications. First, single-cell proteomics, coupled with
approaches derived from statistical physics, can yield detailed (and often surprising)
predictions, which can be experimentally validated. Traditional biology experiments
on bulk cell cultures or disease models rarely yield such detailed predictions. Furthermore,
cellular heterogeneity was not assessed to capture functional phenotypes. Instead
the fluctuations were analyzed to identify a stable state or to point to where that
state was unstable. Although this general concept is not new, the experimental challenge
has been to find approaches that can accurately sample the relevant fluctuations.
Related examples have drawn from model systems using, for example, green fluorescent
protein reporter genes to provide signatures of protein fluctuations. In one such
case [50], time-lapse live-cell microscopy was used to capture specific promoter activity fluctuations
in fibroblast cells. The authors [50] identified switching rates between two stable states within the cells. A major advantage
of the multiplexed platforms, such as SCBCs, is that fluctuations of broadly sampled
signaling networks from primary cells can be measured, allowing predictive applications
to non-model systems, with extensions to clinically relevant problems.

Extending such assays to discrete cell populations (unique to microfluidic/nanotechnology
platforms) allows the investigation of cell-cell interactions. A few studies have
explored the inhibitory or activating nature of such interactions using a combination
of protein assays and/or functional observations [52-55]. A recent study [28] correlated the levels of a panel of phospho- (and effector) signaling proteins in
model GBM cells, with cell-cell distances in two-cell assays. This indicated that
a detailed knowledge of pairwise cell interaction functions could be used to predict
specific properties of larger cell populations. Such experiments again draw from concepts
derived from statistical physics [56], and may eventually allow complex phenomena within tissue microenvironments to be
understood.

Looking forward

The advance of methods for single-cell functional proteomics has been rapid, and the
majority of tools discussed here did not exist 5 years ago. These platforms offer
unique and emerging opportunities. The coupling of functional and proteomic assays
at the single-cell level is one such advantage. Most microfluidic proteomics platforms,
however, cannot yet match the statistics and throughput of cytometry tools. However,
as these technologies evolve, the range of potential applications will continue to
expand, as will the thinking regarding how the resultant datasets can be interpreted.
It is likely that, in the near future, microchip platforms will enable as many as
100 proteins to be assayed from single cells, and platforms that enable 10- to 20-plex
assays will become routine biological and clinical tools. However, beyond about 100
proteins, all (microchip or cytometry) single-cell proteomics approaches will ultimately
be limited by antibodies or other capture agents. Thus, an important underlying challenge
is the production of high-performance and robust protein capture agents at low cost.
A second outstanding challenge is the development of a capture-agent-independent approach
that allows discovery.

One area that has not been covered here is that of mass spectrometry. However, that
field has seen remarkable advances over the past few years, and single-cell proteomics
may be on the horizon. Targeted proteomics using mass spectrometry has evolved to
the extent that small cell numbers, or even single cells, can be analyzed for highly
abundant proteins. Protein processing with immobilized enzymes [57] or novel column chromatography methods [58] may eventually allow mass spectrometry to be a single-cell proteomics discovery tool.
Finally, the idea that single-cell functional proteomics can provide a conduit to
the predictive world of statistical physics is exciting, but the benefits (and limitations)
of this type of thinking are largely untapped. It is certain, however, that as measurement
quantification, multiplexing capacity, statistical sampling, and sensitivity all improve,
so will the power of the models that can use these data to resolve what are otherwise
complex biological problems.

Competing interests

The authors declare that they have no competing interests.

Acknowledgments

Some of the work reviewed here was supported by the National Cancer Institute (5U54
CA119347 and R01 CA170689-01 to JRH) and the National Institutes of Health (NIH 1
U01 CA164252-01 to RF and U54 CA143798 to RF).